The ease of use and cost efficiency have contributed to the growing popularity of digital imaging systems. However, inferior spatial resolution with respect to the traditional film cameras is still a drawback. The apparent aliasing effects often seen in digital images are due to the limited number of CCD pixels used in commercial digital cameras. Using denser CCD arrays (with smaller pixels) not only increases the production cost but also results in noisier images. As a cost efficient alternate, image processing methods have been exploited through the years to improve the quality of digital images. Regression methods that attempt to recover the noiseless high-frequency information corrupted by the limitations of imaging system have been attempted, as well as the degradations processes such as compression.
Besides in-painting applications, interpolation of irregularly sampled image data is essential for applications such as multi-frame super-resolution, where several low-resolution images are fused (interlaced) onto a high-resolution grid. “Denoising” is a special case of the regression problem where samples at all desired pixel locations are given but these samples are corrupted, and are to be restored.
Many methods have been proposed to provide improved images, such as B-spline interpolation, orthogonal series methods, and cubic spline interpolation techniques to name a few. Currently there does not exist a single method that is reliable for image denoising (Gaussian, Film grain, and compression artifacts) and image interpolation (upscaling, image reconstruction from irregularly sampled data sets, and super-resolution). There does not exist one single set of techniques to cover many deficient areas of image reconstruction applications.
Accordingly, there is a need to develop a single method that is reliable for image denoising and image interpolation to overcome the current shortcomings in the art.